Vehicle and method for advising driver of same

ABSTRACT

A method for advising a driver of a vehicle may include measuring a plurality of parameters representing the vehicle&#39;s current handling condition and the vehicle&#39;s limit handling condition, determining a margin between the vehicle&#39;s current handling condition and limit handling condition, and initiating an alert for the driver before the vehicle achieves the limit handling condition if the margin exceeds a predetermined threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national phase of PCT Application No.PCT/US2009/062698, filed Oct. 30, 2009, which claims the benefit of U.S.provisional Application Ser. No. 61/109,584, filed Oct. 30, 2008, thedisclosures of which are hereby incorporated by reference in theirentirety.

BACKGROUND

Driver error is cited as the cause of 45% to 75% of roadway collisionsand as a contributing factor in a majority of all collisions.

Lane-Departure Warning (LDW) uses a vision sensor to detect a vehicle'sposition relative to a lane and warn the driver of an unintentional lanedeparture. Certain Forward Collision Warning (FCW) systems useenvironmental sensors to detect potential safety hazards in front of avehicle and warn the driver in advance. These existing driver warnings,however, operate during steady-state or quasi-steady-state drivingconditions.

SUMMARY

An automotive vehicle may include at least one of an electronicstability control system, anti-lock braking system and traction controlsystem, and at least one computing device. The at least one computingdevice may be configured to initiate an alert for a driver before the atleast one of the electronic stability control system, anti-lock brakingsystem and traction control system activates based on a margin betweenthe vehicle's current handling condition and limit handling condition.

While example embodiments in accordance with the invention areillustrated and disclosed, such disclosure should not be construed tolimit the invention. It is anticipated that various modifications andalternative designs may be made without departing from the scope of theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an embodiment of a vehicle control system.

FIG. 2 is a plot of example vehicle speed, traction and brakingprofiles.

FIGS. 3A through 3C are plots of example vehicle motion states of yawrate and sideslip angle.

FIGS. 4A through 4C are plots of example yawing, longitudinal, andsidesliping handling limit margins.

FIG. 5 is a plot of example vehicle speed, traction and brakingprofiles.

FIGS. 6A through 6C are plots of example vehicle motion states of yawrate and sideslip angle.

FIGS. 7A through 7C are plots of example yawing, longitudinal, andsidesliping handling limit margins.

FIG. 8 is a plot of example membership functions characterizing fourdriver categories based on handling risk factor.

FIGS. 9A, 10A and 11A are plots of example final handling limit marginsand risk.

FIGS. 9B, 10B and 11B are plots of example probabilities of driverstyle.

FIG. 12 is a plot of example determinants of smooth and abrupt drivingbehaviors.

FIGS. 13A and 13B are plots of example mean gap times for aggressive andcautious driving respectively.

FIGS. 14A and 14B are plots of example standard deviations ofaccelerator pedal rate for aggressive and cautious driving respectively.

FIGS. 15A and 15B are plots of example standard deviations of brakepedal rate for aggressive and cautious driving respectively.

FIGS. 16A and 16B are plots of example driver indices for aggressive andcautious driving respectively.

FIG. 17 is a plot of an example relative range, range-error andlongitudinal acceleration between a leading and following car foraggressive driving.

FIG. 18 is a plot of select example parameters characterizing theaggressive driving of FIG. 17.

FIG. 19 is a plot of an example relative range, range-error andlongitudinal acceleration between a leading and following car forcautious driving.

FIG. 20 is a plot of select example parameters characterizing thecautious driving of FIG. 19.

FIGS. 21 through 23 are block diagrams of embodiments of driver advisorysystems.

DETAILED DESCRIPTION I. Introduction

An objective of existing vehicle electronic control systems is tofacilitate the driving task by identifying driver intent and aiding thedriver by controlling the vehicle to achieve the driver intent safely,robustly, and smoothly. The control effectiveness of electronic controlsystems may be significantly increased when the driver and theelectronic control system work together towards the same accidentavoidance goal and maximize the accident avoidance capability of thedriver-in-the-loop vehicle as a system. One approach to achieve this isto provide timely clear and transparent advisory information to a driversuch that a responsible driver can respond accordingly. Such advisoryinformation can be gathered or computed from sensors normally found on avehicle, which implement a bilateral closed-loop control between thedriver and the electronic control. The electronic control follows thedriver intent and the driver responds to the advisory information fromthe electronic control to modify his drive inputs (such as droppingthrottle, easing steering inputs, etc.) In this way, a seamlesscoordination between the driver and the electronic control system ispossible and is likely to minimize the effect of potential safetyhazards due to driver errors.

We consider, inter alia, warnings that occur close to the handlinglimit, a driving or maneuvering condition in which vehicle stabilitycontrols usually intervene. In addition to problems encountered near thehandling limit, the driver advisory system approach discussed herein canalso be used to improve fuel economy, i.e., a system which can useadvising and/or coaching to help the driver learn driving habits thatconserve fuel. We also discuss using data from vehicle stabilitycontrols to provide real-time warnings when the vehicle approaches thehandling limit. This may be part of a cluster of warning functions thatmay be defined as an Intelligent Personal Minder (IPM) system. Generallyspeaking, the intelligence computed for the IPM system can be sent towarn or advise a driver through various devices including a hapticpedal, a heads-up-display, an audio warning device, a voice system, etc.

FIG. 1 depicts the interaction of an embodiment of an IPM system 10 withother components/subsystems 12 of a vehicle 14. The othercomponents/subsystems 12 may include vehicle sensors 16, (e.g., a yawrate sensor, steering angle sensor, lateral acceleration sensor,longitudinal acceleration sensor, wheel speed sensor, brake pressuresensor, etc.), actuators 20 and one or more controllers 22. The one ormore controllers 22 may include stability control 24, arbitration logic26 and other controllers/systems 28 (e.g., anti-lock braking system,traction control system, etc.)

For any control system, the plant model may play a role in designing aneffective control strategy. Similarly, a driver model is important forgenerating effective and appropriate driver advisory signals. Hence,driving style characterization may be needed. We discuss methods ofidentifying a driver's characteristics based on his or her vehiclehandling capability. While driver modeling and driver behaviorcharacterization have been studied, we suggest an approach in which thedriving behavior/style and/or the driving experience level may bededuced, for example, based on the frequency and duration of drivingclose to the handling limit (as well as other techniques). Such drivercharacterization information may be used in a variety of applications,some of which are discussed below.

II. A Brief Discussion Of Vehicle Stability Controls

A vehicle's handling determines the vehicle's ability to corner andmaneuver. The vehicle needs to stick to the road with its four tirecontact patches in order to maximize its handling capability. A tirewhich exceeds its limit of adhesion is either spinning, skidding orsliding. A condition where one or more tires exceed their limits ofadhesion may be called a limit handling condition and the adhesion limitmay be called a handling limit. Once a tire reaches its handling limit,the average driver is usually no longer in control. In the so-calledundersteer case, the car under performs a driver's steering input, itsfront tires pass their handling limit, and the vehicle continues goingstraight regardless of the driver's steering request. In the so-calledoversteer case, the car over performs the driver's steering inputs, itsrear tires pass their handling limit, and the vehicle continuesspinning. For safety purposes, most vehicles are built to understeer attheir handling limits.

In order to compensate vehicle control in case a driver is unable tocontrol the vehicle at or beyond the handling limit, electronicstability control (ESC) systems are designed to redistribute tire forcesto generate a moment that can effectively turn the vehicle consistentwith the driver's steering request. Namely, to control the vehicle toavoid understeer and oversteer conditions.

Since its debut in 1995, ESC systems have been implemented in variousplatforms. Phasing in during model year 2010 and achieving fullinstallation by model year 2012, Federal Motor Vehicle Safety Standard126 requires ESC systems on any vehicle with a gross weight rating below10,000 lb. ESC systems may be implemented as an extension of anti-lockbraking systems (ABS) and all-speed traction control systems (TCS). Theymay provide the yaw and lateral stability assist to the vehicle dynamicscentered around the driver's intent. It may also proportion brakepressure (above or below the driver applied pressure) to individualwheel(s) so as to generate an active moment to counteract the unexpectedyaw and lateral sliding motions of the vehicle. This leads to enhancedsteering control at the handling limits for any traction surface duringbraking, accelerating or coasting. More specifically, current ESCsystems compare the driver's intended path to the actual vehicleresponse which is inferred from onboard sensors. If the vehicle'sresponse is different from the intended path (either understeering oroversteering), the ESC controller applies braking at selected wheel(s)and reduces engine torque if required to keep the vehicle on theintended path and to minimize loss of control of the vehicle.

A limit handling condition can be detected using data already existingin ESC systems, so new sensors may not be required. Consider, forexample, a vehicle equipped with an ESC system using a yaw rate sensor,a steering wheel sensor, a lateral accelerometer, a wheel speed sensors,a master cylinder brake pressure sensor, a longitudinal accelerometer,etc. The vehicle motion variables are defined in the coordinate systemsas defined in ISO-8855, where a frame fixed on the vehicle body has itsvertical axis up, longitudinal axis along the longitudinal direction ofthe vehicle body, and a lateral axis pointed from the passenger side tothe driver side.

Generally speaking, vehicle level feedback controls can be computed fromindividual motion variables such as yaw rate, sideslip angle, or theircombination together with arbitrations among other control commands suchas driver braking, engine torque request, ABS and TCS. Vehicle levelcontrol commands are discussed in the following.

The well-known bicycle model captures the vehicle dynamics, its yaw rateω_(z) along the vertical axis of the vehicle body and its sideslip angleβ_(r) defined at its rear axle, and obeys the following equationsI _(z){dot over (ω)}_(z) =−b _(f) c _(f)(μ_(r) +bω _(zt) v _(x) ⁻¹−δ)+b_(r) c _(r)β_(r) +M _(z)M({dot over (v)} _(x)β_(r) +v _(x){dot over (β)}_(r) +b _(r){dot over(ω)}_(z)+ω_(z) v _(x))=−c _(f)(β_(r) +bω _(z) v _(x) ⁻¹−δ)−c_(r)β_(r)  (1)where v_(x) is the vehicle's travel speed, M and I_(z) are the totalmass and the yaw moment of inertia of the vehicle, c_(f) and c_(r) arethe cornering stiffness of the front and rear tires, b_(f) and b_(r) arethe distances from the center of gravity of the vehicle to the front andrear axles, b=b_(f)+b_(r), M_(z) is the active moment applied to thevehicle, and δ is the front wheel steering angle.

A target yaw rate ω_(zt) and a target sideslip angle β_(rt) used toreflect the driver's steering intent can be calculated from (1) usingthe measured steering wheel angle δ and the estimated travel velocityv_(x) as the inputs. In such a computation, we assume that the vehicleis driven on a road of normal surface condition (e.g., high frictionlevel with nominal cornering stiffness c_(f) and c_(r)). Signalconditioning, filtering and nonlinear corrections for steady state limitcornering may also be performed in order to fine tune the target yawrate and the target sideslip angle. These calculated target valuescharacterize the driver's intended path on a normal road surface.

The yaw rate feedback controller is essentially a feedback controllercomputed from the yaw error (the difference between the measured yawrate and the target yaw rate). If the vehicle is turning left andω_(z)≧ω_(zt)+ω_(zdbos) (where ω_(zdbos) is a time varying deadband), orthe vehicle is turning right and ω_(z)≦ω_(zt)−ω_(zdboz), the vehicle isoversteering and activating the oversteer control function in ESC. Forinstance, the active torque request (applied to the vehicle for reducingthe oversteer tendency) might be computed as in the followingduring a left turn: M _(z)=min(0,−k _(os)(ω_(z)−ω_(zt)−ω_(zdbos)))during a right turn: M _(z)=max(0,−k _(os)(ω_(z)−ω_(zt)+ω_(zdbos)))  (2)where k_(os) is a speed dependent gain which might be defined as in thefollowing

$\begin{matrix}{k_{os} = {k_{0} + {( {v_{x} - v_{xdbl}} )\frac{\;{k_{dbu} - k_{dbl}}}{v_{xdbu} - v_{xdbl}}}}} & (3)\end{matrix}$with parameters k₀, k_(dbl), k_(dbu), v_(xdbl), v_(xdbu) being tunable.

If ω_(z)≦ω_(z)−ω_(zdbus) (where ω_(zdbus) is a time varying deadband)when the vehicle is turning left or ω_(z)≧ω_(z)+ω_(zdbus) when thevehicle is turning right, the understeer control function in ESC isactivated. The active torque request can be computed as in the followingduring a left turn: M _(z)=max(0,−k _(us)(ω_(z)−ω_(zt)+ω_(zdbus)))during a right turn: M _(z)=min(0,−k _(us)(ω_(z)−ω_(zt)ω_(zdbus)))  (4)where k_(us) is a tunable parameter.

The sideslip angle controller is a supplementary feedback controller tothe aforementioned oversteer yaw feedback controller. It compares thesideslip angle estimation β_(r) to the target sideslip angle β_(rt). Ifthe difference exceeds a threshold β_(rdb), the sideslip angle feedbackcontrol is activated. For instance, the active torque request iscalculated as in the followingduring a left turn,β_(r)≧0:M _(z)=min(0,k _(ss)(β_(r) −B _(rt) −B _(rdb))−k _(sscmp),{dotover (β)}_(rcmp))during a right turn,β_(r)<0:M _(z)=max(0,k _(ss)(β_(r) −B _(rt) +B _(rdb))−k _(sscmp){dotover (β)}_(rcmp))  (5)where k_(ss) and k_(sscmp) are tunable parameters and {dot over(β)}_(rcmp) is a compensated time derivative of the sideslip angle.

Other feedback control terms based on variables such as yaw accelerationand sideslip gradient can be similarly generated. When the dominantvehicle motion variable is either the yaw rate or the sideslip angle,the aforementioned active torque can be directly used to determine thenecessary control wheel(s) and the amount of brake pressures to be sentto corresponding control wheel(s). If the vehicle dynamics are dominatedby multiple motion variables, control arbitration and prioritizationwill be conducted. The final arbitrated active torque is then used todetermine the final control wheel(s) and the corresponding brakepressure(s). For example, during an oversteer event, the front outsidewheel is selected as the control wheel, while during an understeerevent, the rear inside wheel is selected as the control wheel. During alarge side-slipping case, the front outside wheel is always selected asthe control wheel. When both the side slipping and oversteer yawinghappen simultaneously, the amount of brake pressure may be computed byintegrating both yaw error and the sideslip angle control commands.

Besides the above cases where the handling limit is exceeded due to thedriver's steering maneuvers, a vehicle can reach its limit handlingcondition in its longitudinal motion direction. For example, braking ona snowy and icy road can lead to locked wheels, which increases thestopping distance of the vehicle. Open throttling on a similar road cancause the drive wheels to spin without moving the vehicle forward. Forthis reason, the handling limit may also be used for these non-steeringdriving conditions. That is, the conditions where the tire longitudinalbraking or driving forces reach their peak values may also be includedin a definition of the handling limit.

The ABS function monitors the rotational motion of the individual wheelsin relation to the vehicle's travel velocity, which can be characterizedby the longitudinal slip ratios λ_(i), with i=1, 2, 3, 4 for thefront-left, front-right, rear-left and rear-right wheels, computed as inthe following

$\begin{matrix}{{\lambda_{1} = {\frac{\kappa_{1}\omega_{1}}{\max( {{{( {v_{x} + {\omega_{z}t_{f}}} ){\cos(\delta)}} + {( {v_{y} + {\omega_{z}b_{f}}} ){\sin(\delta)}}},v_{m\; i\; n}} )} - 1}}{\lambda_{2} = {\frac{\kappa_{2}\omega_{2}}{\max( {{{( {v_{x} + {\omega_{z}t_{f}}} ){\cos(\delta)}} + {( {v_{y} + {\omega_{z}b_{f}}} ){\sin(\delta)}}},v_{{m\; i\; n}\;}} )} - 1}}{{\lambda_{3} = {\frac{\kappa_{3}\omega_{3}}{\max( {{v_{x} - {\omega_{z}t_{r}}},v_{m\; i\; n}} )} - 1}},{\lambda_{4} = {\frac{\kappa_{4}\omega_{4}}{\max( {{v_{x} + {\omega_{z}t_{r}}},v_{m\; i\; n}} )} - 1}}}} & (6)\end{matrix}$where t_(f) and t_(r) are the half tracks for the front and rear axles,ω_(i) is the i^(th) wheel speed sensor output, κ_(i) is the i^(th) wheelspeed scaling factor, v_(y) is the lateral velocity of the vehicle atits c.g. location, and v_(min) is a preset parameter reflecting theallowable minimum longitudinal speed. Notice that (6) is only valid whenthe vehicle is not in the reverse driving mode. When the driverinitiated braking generates too much slip (e.g., −λ_(i)≧λ_(bp)=20%) at awheel, the ABS module will release the brake pressure at that wheel.Similarly, during a large throttle application causing a large slip onthe i^(th) driven wheel, the TCS module will request engine torquereduction and/or brake pressure applied to the opposite wheel at thesame axle. Consequently, ABS or TCS activations can be predicted bymonitoring how close t_(i)s are from λ_(bp) and λ_(tp).

III. Handling Limit Minder

While the aforementioned ESC (including ABS and TCS) is effective inachieving its safety goal, further enhancement is still possible. Forexample, augmentation of ESC systems may be desirable for roll stabilitycontrol. The appropriate correction which ESC tries to make, however,may be counteracted by the driver or ambient conditions. A speedingvehicle, whose tire forces go far beyond the traction capability of theroad and the tires, might not be able to avoid an understeer accidenteven with ESC intervention.

We introduce an integration of the driver and ESC system such that theymay work cooperatively towards an enhanced control performance of thedriver-in-the-loop system. In certain embodiments, the proposed HandlingLimit Minder (HLM) determines how close the current driving condition isto the handling limit.

Generally speaking, accurate determination of the handling limitconditions would involve direct measurements of road and tirecharacteristics or very intensive information from many relatedvariables if direct measurements are not available. Currently, both ofthese methods are not mature enough for real-time implementation.

Due to their feedback feature, ESC systems may be configured todetermine the potential limit handling conditions through monitoring themotion variables of a vehicle such as those described in the lastsection. When the motion variables deviate from their reference valuesby a certain amount (e.g., beyond certain deadbands), the ESC systemsmay start to compute differential braking control command(s) anddetermine control wheel(s). The corresponding brake pressure(s) is thensent to the control wheel(s) to stabilize the vehicle. The startingpoint of the ESC activation can be thought of as the beginning of thehandling limit.

More specifically, we may define a relative handling limit margin h_(x)as in the following

$\begin{matrix}{h_{x} = \{ \begin{matrix}\frac{\overset{\_}{x} - x}{\overset{\_}{x}} & {{{if}\mspace{14mu} 0} \leq x \leq \overset{\_}{x}} \\\frac{x - \underset{\_}{x}}{\underset{\_}{x}} & {{{if}\mspace{14mu}\underset{\_}{x}} \leq x < 0} \\0 & {otherwise}\end{matrix} } & (8)\end{matrix}$where x is the deviation of a motion variable from its reference valueand [x,x] defines the deadband interval within which x falls withoutinitiating the ESC, ABS or TCS. x can be any of the control variablesdefined in the last section (or any other suitable control variable).

The benefit of h_(x) defined in (8) is that the driving condition can bequantitatively characterized into different categories. For instance,when h_(x)≦10%, the driving condition may be categorized as a red zonecondition where the driver needs to have special attention or take somespecial actions (e.g., slowing down the vehicle); when 10%<h_(x)<40%,the driving condition may be categorized as a yellow zone conditionwhich needs some level of special attention from the driver; when40%<h_(x)≦100%, the driving condition may be characterized as a normalcondition. In the normal condition, the driver need only to maintain hisnormal driving attention. Of course, other ranges may also be used.

Various audible and/or visual warnings may be activated to alert adriver about the handling limit margin. When h_(x)≦10% for example, awarning light/haptic device may activate to inform the driver that theyneed to slow down. Alternatively, a voice enabled display system mayinstruct the driver to take particular action. When 10%<h_(x)<40%, anaudible tone or display may inform the driver that they are approachingunstable driving conditions, etc.

More specifically, let us use the control variables computed in the lastsection to discuss the computation of h_(x)s. The vehicle's yaw handlinglimit margin during oversteer situations h_(OS) (where ω_(z)>ω_(zt) whenthe vehicle is turning to the left and ω_(z)>ω_(zt) when the vehicle isturning to the right) can be computed from (8) by setting x=ω_(z)−ω_(zt)and x=ω_(zdbos)=−x, where ω_(zdbos) is the oversteer yaw rate deadbandas defined in (2).

Similarly, the vehicle's yaw handling limit h_(US) for understeersituations can be computed from (8) by setting x=ω_(z)−ω_(zt) andx=ω_(zdbus)=−x, where ω_(zdbus) is the understeer yaw rate deadband asdefined in (4). Notice that the aforementioned deadbands might befunctions of the vehicle speed, the magnitude of the target yaw rate,the magnitude of the measured yaw rate, etc. The deadbands for theundersteer situation (x<0) and the oversteer situation (x>0) aredifferent and they are tunable parameters.

The vehicle's sideslip handling limit margin h_(SSRA) can be computedfrom (8) by setting x=β_(r)−β_(rt) and x=β_(rdb)=−x.

The longitudinal handling limits of the vehicle involve the conditionswhen either the driving or braking force of the tires approach thehandling limit. The traction control handling limit margin for thei^(th) driven wheel h_(TCS) _(i) can be computed from (8) by settingx=λ_(i), x=0, and x=λ_(tb). The ABS handling limit margin for the i^(th)wheel h_(ABS) _(i) can also be computed from (8) by setting x=λ_(i),x=λ_(bp), and x=0. The final traction and braking handling limit marginsmay be defined as

$\begin{matrix}{{h_{{AB}\; S} = {\min\limits_{i \in {\{{1,2,3,4}\}}}h_{{AB}\; S_{i}}}},{h_{TCS} = {\min\limits_{i \in {\{{1,2,3,4}\}}}h_{{TCS}_{i}}}}} & (9)\end{matrix}$

Notice that further screening conditions may be used in computing theaforementioned handling limit margins. For instance, one of thefollowing or the combination of some of the following conditions mightbe used to set the handling limit margin as 0: a magnitude of the targetyaw rate is beyond a certain threshold; a magnitude of the measured yawrate is greater than a certain threshold; a driver's steering inputexceeds a certain threshold; or, extreme conditions such as thevehicle's cornering acceleration is greater than 0.5 g, the vehicle'sdeceleration is greater than 0.7 g, the vehicle is driven at a speedbeyond a threshold (e.g., 100 mph), etc.

In order to test the aforementioned handling limit margin computationsand verify their effectiveness with respect to known driving conditions,a vehicle equipped with a research ESC system developed at Ford MotorCompany was used to conduct vehicle testing.

For the driving condition profiled by the vehicle speed, throttling, andbraking depicted in FIG. 2, the measured and computed vehicle motionvariables are shown in FIGS. 3A through 3C. The corresponding individualhandling limit margins h_(US), h_(OS), h_(TCS), h_(ABS), and h_(SSRA)are shown in FIGS. 4A through 4 c. This test was conducted as a freeform slalom on a snow pad with all ESC computations running. The brakepressure apply was turned off in order for the vehicle to approach thetrue limit handling condition.

For another test, the vehicle was driven on a road surface with highfriction level. The vehicle speed, traction and braking profiles forthis test are depicted in FIG. 5. The vehicle motion states are shown inFIGS. 6A through 6C. The corresponding individual handling limit marginsh_(US), h_(OS), h_(TCS), h_(ABS), and h_(SSRA) are shown in FIGS. 7A and7B.

An envelope variable of all the individual handling limit margins isdefined ash _(env)=min {h _(OS) ,h _(US) ,h _(TCS) ,h _(ABS) ,h _(SSRA)}  (10)Considering that sudden changes in the envelope handling limit marginmight be due to signal noises, a low-pass filter F(z) is used to smoothh_(env) so as to obtain the final handling limit marginh=F(z)h _(env)  (11)For the vehicle test data shown on FIG. 2 and FIGS. 3A through 3C, thefinal handling limit margin is depicted in FIG. 9A, while for thevehicle test data shown on FIG. 5 and FIGS. 6A through 6C, the finalhandling limit margin is depicted in FIG. 10A.

IV. Handling Limit Driving Style Characterization

In this section, we use the final handling limit margin computed in (11)to characterize vehicle handling related driving conditions and drivingstyle. We introduce the concept of a Handling Risk Factor (HRF) as themeasure of how a driving condition is related to the handling limit. Thehandling risk factor r is defined as the complement of the finalhandling limit margin h, i.e.,r=1−h  (12)

The handling risk factor is minimal (r=0) when the final handling limitmargin h is maximal (h=1) and vice versa. The HRF may be further used todevelop a probabilistic model describing different categories of drivingstyles which are reflected by the current driving conditions withrespect to the handling limit.

Generally speaking, a cautious driver usually drives without frequentaggressiveness, i.e., fast changes of steering, speed and acceleration.Hence, it is reasonable to characterize a cautious driver as one whoconstantly avoids using extreme driving inputs and getting close to themaximal handling risk. An average driver likely exhibits a higher levelof HRF than a cautious driver. An expert driver might be more skilful incontrolling the vehicle, i.e., he can drive with a relatively high levelof HRF for a long duration without having the vehicle pass the maximalhandling limit. A reckless driver exhibits a careless handling behaviorwhich is unpredictable and could induce fast changes. The recklessdriver is expected to drive with a handling risk factor that mightapproach the maximum (r=1) very briefly from time to time, thus causingfrequent triggering of the related safety systems (e.g., ABS, TCS, ESC).

Notice that the difference between the expert driver and the recklessdriver is that the former can hold a driving condition at a relativelyhigh HRF level for long duration, while the latter can only hold at thesimilar level for a short duration before causing the vehicle to passthe maximal handling limit due to the driver's poor control capability.Since the handling risk factor ranges defining, for example, cautious,average, expert and reckless driving behavior (with respect to the limithandling conditions) may not be well defined, we use fuzzy subsets toquantify the four categories of drivers. We further evaluate thosecategories probabilistically based on a specific driver style. The fuzzysubsets associated with the categories of cautious, average, expert andreckless drivers may be described by the following membership functionsμ_(c)(r),μ_(e)(r),μ_(a)(r),μ_(r)(r)defined over the HRF universe [0, 1]. FIG. 8 shows the relationshipbetween the degrees of membership for each of those categories and theHRF.

The membership functions in FIG. 8 can be assigned to any event that isrepresented by a specific HRF with value r_(k) using a four dimensionalvectorD _(k)=[μ_(c)(r _(k))μ_(e)(r _(k))μ_(a)(r _(k))μ_(r)(r _(k))]^(T)of its degree of membership to each of the four example categories:cautious, average, expert and reckless. For example, a HRF valuer_(k)=0.4 (corresponding to handling limit margin value h_(k)=0.6) willtranslate to the degrees of membership to the cautious, average, expertand reckless categoriesμ_(c)(0.4)=0.46,μ_(e)(0.4)=0.85μ_(a)(0.4)=0.09,μ_(r)(0.4)=0.22

The membership grades encode the possibilities that the eventcharacterized by a HRF with value r=0.4 (or the handling limit marginh=0.6) might be associated with any of the four example partitions. Thevector of membership values d_(k) makes the association between a singledriving event and the possible driver characterization with respect tothe HRF of that event. In order to characterize the long term behaviorof the driver, we need a probabilistic interpretation of thepossibilities that are generated by multiple events. By adding themembership values for each event, we essentially aggregate the overallpossibilities that a specific driver can be categorized as cautious,average, expert and reckless, i.e., the vector

$\begin{matrix}{d^{*} = {\sum\limits_{k = 1}^{N}\lbrack {{\mu_{c}( r_{k} )}{\mu_{e}( r_{k} )}{\mu_{r}( r_{k} )}} \rbrack^{T}}} & (13)\end{matrix}$where N is the number of samples. The aggregated possibilities can beconsidered as frequencies (sometimes referred to as fuzzy frequencies)since they reveal how frequently and to what degree the HRFs for themultiple events can be cascaded to the four example categories. Thealternative to aggregating the possibilities, i.e., adding themembership functions, is to add 1 if the specific membership gradeμ_(i)(r_(k),iε{c,a,e,r} is greater than a prescribed threshold value,e.g., 0.8, or 0 otherwise, resulting in calculating the conventionalfrequency of the four example categories. From the aggregatedpossibilities, we can calculate the probabilities of the cautious,average, expert and reckless driver style

$\begin{matrix}{p_{i} = {d_{i}^{*}( {\sum\limits_{j \in {\{{c,a,e,r}\rbrack}}d_{j}^{*}} )}^{- 1}} & (14)\end{matrix}$where iε{c,a,e,r}. The probabilities are calculated from the aggregatedpossibilities (fuzzy frequencies) and can be considered fuzzyprobabilities. The reason for the fuzziness here is the lack ofcertainty in characterizing the relationship between the four examplecategories and the HRF. For the special case of crisply definedcategories (represented by intervals rather than fuzzy subsets), thepossibilities transform to Boolean values, their aggregated valuesbecome frequencies, and consequently the fuzzy probabilities aretranslated to conventional probabilities.

The most likely driver category i* is the one that is characterized withthe highest probability, i.e.,

$\begin{matrix}{i^{*} = {\arg\limits_{i \in {\{{c,a,e,r}\}}}{\max( p_{i} )}}} & (15)\end{matrix}$

The frequencies based calculation of the probabilities can be expressedin terms of the average frequencies

$\begin{matrix}{p_{i} = {d_{i}^{*}/{N( {\sum\limits_{j \in {\{{c,a,e,r}\}}}{d_{j}^{*}/N}} )}^{- 1}}} & (16)\end{matrix}$Alternatively, it can be expressed through the exponentially weightedaverage frequencies where the higher weights are assigned to thepossibilities that are associated with the most recent events.Numerically, the process of generating a weighted average with higherweights corresponding to the recent observation can be accomplished byapplying a low pass filter implementing the exponential smoothingalgorithm in the time domaind _(new)*(1−α)d _(old) *+αd _(k) =d _(old)*+α(d _(k) −d _(old))  (17)where the constant forgetting factor 0<α≦1 controls the rate of updatingthe mean d* by assigning a set of exponentially decreasing weights tothe older observations. For a constant forgetting factor α, expression(17) recursively generates a vector of positive weightsW=[(1−α)^(k)α(1−α)^(k−1)α(1−α)^(k−2) . . . α]  (18)with a unit sum. Vector W delineates a weighted average type aggregatingoperator with exponentially decreasing weights that are parameterized bythe forgetting factor α. Parameter α defines the memory depth (thelength of the moving window) of the weighted averaging aggregatingoperator. Therefore, the filtered value d* of the membership gradevector in (17) represents the weighted averages of the individualpossibilities over the weights W. Since all of the aggregatedpossibilities are calculated over the same moving window of lengthK_(α)=1/α, we can consider them as representations of the frequencies ofthe associations with each of the four concepts. Weighted average (17)is calculated over the events with indexes belonging to a soft intervalsε{k−K _(α)+1,k]  (19)where symbol { indicates a soft lower bound that includes values withlower indexes than (k−K_(α)) with relatively low contribution.Consequently, the aggregated possibilities that form the vector d* canbe converted to probabilities according to expression (14).

In certain embodiments, α may be selectable so that a characterizationfor a desired period of time is achieved. For example, a user may supplyinput related to α such that every half hour, a characterization ismade. Other scenarios are also possible.

For the vehicle testing depicted by FIGS. 2, 3A through 3C and 4Athrough 4C, the individual p_(i)'s are shown on FIG. 9B indicating thatfor most of the driving, the driver exhibited a reckless drivingbehavior, which is consistent with the large value of the sideslip anglein FIG. 3C (peak magnitude of the sideslip angle exceeds 10 degrees).For vehicle testing depicted by FIGS. 5 through 7C, the individualp_(i)'s are shown in FIG. 10B, indicating that the driver initiallyexhibited an average driver behavior and then transitioned to a recklessdriver behavior.

The calculated probabilities define the most likely HRF basedcharacterization of a driver for the time window that is specified bythe forgetting factor α. By modifying the moving window, we can learnand summarize the long and short term characterization for a specificdriver based on the HRF.

In order to predict the impact of the changes in HRF on the driver'scharacterization, we introduce the notion of transitional probabilities.The Markov model P probabilistically describes the set of transitionsbetween the current and the predicted value of the driver category:

$\mspace{135mu}{{p_{j}( {k + 1} )}->\begin{matrix}\; & p_{11} & p_{12} & p_{13} & p_{14} \\{p_{i}(k)} & p_{21} & p_{22} & p_{23} & p_{24} \\\; & p_{31} & p_{32} & p_{33} & p_{34} \\\; & p_{41} & p_{42} & p_{43} & p_{44}\end{matrix}}$where p_(ij) is the probability of switching from category i at time kto category j at time k+1, and p_(ii)=max(p_(i)) is the probability thatis associated with the dominating category i at time k, i,jε{c,a,e,r}.The transitional probabilities p_(ij) are derived from the transitionalaggregated possibilities that are updated only if i=arg max (p_(l)) attime k and j=arg max (p_(l)), lε{c,a,e,r}

$\begin{matrix}{d_{{if},{new}}^{*} = \begin{Bmatrix}{{( {1 - \alpha} )d_{{if},{old}}^{*}} + {\alpha\; d_{i,k}}} & {{{if}\mspace{14mu} j} = {\arg\limits_{i \in {\{{c,a,e,r}\}}}{\max( p_{l} )}}} \\{( {1 - \alpha} )d_{{if},{old}}^{*}} & {otherwise}\end{Bmatrix}} & (20)\end{matrix}$The transitional probabilities are then calculated by converting theaggregated transitional possibilities to the probabilities. The maximaltransitional probability p_(ij) determines the transition from categoryi to category j as the most likely transition.

FIGS. 11A and 11B use a driving vehicle test to verify long term drivingbehavior characterization. The driver generally shows a cautious drivingstyle (which could be a novice, average or expert driver). At around 190seconds, the vehicle was turned with some degree of aggressiveness whichcan be seen from the peak at the HRF plot, and the driving styletransitioned to the average category. Since no major HRF events werefurther identified, this category was carried out for the rest of thedriving cycle in conjunction with the concept of the long termcharacterization.

As mentioned above, various audible and/or visual warnings may beactivated to alert a driver about the handling limit margin. The marginthresholds that define whether (and/or what type) of warning is to beissued may be altered (or suspended) based on the drivercharacterization. For example, if it is determined that the driver is anexpert driver, the warning threshold may be reduced from h_(x)≦10% toh_(x)≦2%, or the warnings regarding the handling limit margin may besuspended (an expert driver may not need the warnings).

V. Unsupervised Driving Style Characterization

During a normal driving maneuver, a driver's long term longitudinalvehicle control may be used to determine driving behavior regardless ofthe vehicle's dynamic response. For instance, a driver may exhibit aspecific longitudinal control pattern during driving on a highway for along period of time. His pattern of acceleration pedal activation can besmooth or abrupt even in the absence of emergency conditions. Thevariability of the pedal and its rate change can be used todifferentiate between smooth and abrupt application. Such a smooth orabrupt application exhibits strong correlation with fuel economy andacceleration performance when driving conditions are unconstrained.Identifying such driving behaviors can be used, for example, toimplement a fuel economy advisor.

Anomaly detection can be used to estimate major changes in the overallvariability of the control actions indicating changes in correspondingbehaviors. Anomaly detection is a technique that puts a major emphasison the continuous monitoring, machine learning, and unsupervisedclassification to identify a trend of departure from a normal behaviorand predict potential significant change. The determinant of thecovariance matrix of the population of a driver's actions may be used asa measure of the generalized variance (spread) of the population, andhence as an indicator for a change in the driver's behavior.

The feature space of driving torque request τ_(d) and its derivative isspanned by the vector y=[τ_(d){dot over (τ)}_(d)]. The determinant D ofthe covariance matrix of the population can be recursively calculated asD _(k+1)=(1−α)^(k−1) D _(k)(1−α+(y _(k) −v _(k))Q _(k)(y _(k) −v_(k))^(T))  (21)withv _(k+1)=(1−α)v _(k) +αy _(k)Q _(k+1)=(I−G _(k)(y _(k) −v _(k)))Q _(k)(1−α)⁻¹G _(k+1) =Q _(k)(y _(k) −v _(k))^(T)α(1−α+α(y _(k) −v _(k))Q _(k)(y _(k)−v _(k))^(T))⁻¹  (22)where v_(k) is a filtered version of Y_(k), Q_(k) is the estimatedinverse covariance matrix, and α is a constant which reflects theforgetting factor related to the filter memory depth.

D_(k) thus computed in (21) has initial means and standard deviationsfor abrupt and smooth type behaviors. The instantaneous behavior isclassified as abrupt if its value is higher than a control limitl_(abrupt) and is classified as smooth if its value is lower than acontrol limit u_(smooth)·l_(abrupt) and u_(smooth) are defined asl_(abrupt)=μ_(abrupt)−3σ_(abrupt), μ_(smooth)=μ_(smooth)+3σ_(smooth)where μ_(abrupt) and σ_(abrupt) are the mean and standard deviation ofthe abrupt behavior class. μ_(smooth) and σ_(smooth) are similarlydefined for the smooth behavior class. If the current behavior isclassified as either abrupt or smooth, the corresponding mean andstandard deviation of the matching behavior are recursively updatedw _(k+1)=(1−β)w _(k) +βD _(k+1)H _(k+1)=(1−β)H _(k)+(β−β²)(D _(k+1) −w _(k))^(T)(D _(k+1) −w _(k))σ_(k+1)=(H _(k+1))^(1/2)  (23)where w and H are the estimated mean and variance, and β is anotherforgetting factor.

FIG. 12 shows the determinant of the covariance matrix from the vectorof acceleration pedal position and its rate change for 8 runs of vehicletests. The 4 runs with solid lines of determinant were for abruptacceleration pedal applications. These determinants show a large value,for example, greater than 7. The 4 runs with dotted lines of determinantwere for smooth acceleration pedal applications. These determinants showa small value, for example, less than 4. Hence, the size of thedeterminant reveals the unique informative patterns which can be used todistinguish smooth driving behavior from abrupt driving behavior.

Since interactions between the driver and the driving environmentinclude frequent vehicle stops with varied durations, suspension of thecontinual updating may be required to prevent numerical problems duringrecursive computation. The following suspension conditions may be used:(i) If vehicle speed is less than 1 mph, vehicle speed and accelerationrelated recursive calculations are suspended. (ii) If the acceleratorpedal position is less than 1%, pedal related recursive calculations aresuspended.

Although the above deviation focuses on the acceleration pedal, it canbe easily applied to the braking case. Since sudden aggressive brakingcan happen during emergency situations (which are not necessarilyindicative of the driver's general behavior), quasi-steady state drivingwhere braking is not at its extreme may be used for computationalscreening.

During transient acceleration and deceleration, certain wheels of thevehicle may experience large longitudinal slip, and the tirelongitudinal forces of those wheels may reach their peak values. Suchconditions can be identified through monitoring the rotational motion ofthe individual wheels in relation to the vehicle's travel velocity, andconsequently the driver behavior during transient maneuvers can bedetermined as discussed above.

VI. Semi-Supervised Driving Style Characterization

All driver inputs may not be accessible by electronic control systems.Certain variables, however, may construct an input-output pair that maybe used to deduce driver control structure. For example, during acar-following maneuver, the relative distance between the leading andfollowing car and the driver's braking and throttle requests are usuallywell coordinated. Here we consider using a Tagaki-Sugeno (TS) model torelate the variance of the driver's braking and throttling commands tothe relative range and velocity between the leading and following car.

A fuzzy system may utilize the signal conditioned mean driving headway(gap-time) relative to the other vehicle, as well as the standarddeviation of the rate changes of the accelerator pedal and brake pedalto determine whether the driver is aggressive or cautious. The driverindex value from fuzzy computation and rule evaluation may determine theaggressiveness of the driver based on car following, vehicle speed andthe driver's control actions on acceleration and deceleration.

For real-time vehicle implementation, the recursive estimation of themean and variance of a variable of interest is applied. The signalconditioned average mean gap-time at sample time k can be computed asg _(k) =g _(k−1)+α(Δ_(s) _(k) /v _(fk) −g _(k−1))  (24)where Δs_(k) is the relative distance between the leading and followingvehicle, and v_(fk) is the velocity of the following vehicle. α is afilter coefficient similar to the one used in (22). FIGS. 13A and 13Bshow the mean gap-times computed from two runs of vehicle testing: onefor aggressive driving and the other for cautious driving.

The acceleration pedal rate mean can be computed asρ _(k)=ρ _(k−1)+α((ρ_(k)−ρ_(k−1))/ΔT−p _(k−1))  (25)where ρ is the acceleration pedal mean and ΔT is the sample time. Thecorresponding variance can be computed asυ_(k)αυ_(k−1)+(1−α)(ρ_(k)−ρ _(k))²  (26)and the standard deviation is obtained from the square-root of thevariance. FIGS. 14A and 14B show the standard deviations of two runs oftest data for aggressive and cautious driving.

Similar to (25) and (26), the mean and variance of the brake pedal ratechange can be computed. FIGS. 15A and 15B show the standard deviationsof two runs of test data for aggressive and cautious driving. Thevariables are first normalized before presenting them to the fuzzyinference system. The fuzzy sets and membership functions weredetermined for the features to transform the crisp inputs into fuzzyterms. The mean gap-time fuzzy set G_(s) is defined byG _(s)={(g,μ(g))|gεG}  (27)where G is given by the bounded collection of gap-times g in the vehiclepath. The gap-time membership function μ is chosen to be a Gaussianfunction.

A zero-order TS model was used to compute the driver index level. Anormalized output scale from 0-1.0 represented the levels from cautious,to less aggressive, to aggressive driving behavior. The driver index isobtained from fuzzy computation and rule evaluation. Table 1 shows therules used. Notice that a higher gap-time is relatively more safetyconscious compared to a lower gap-time.

TABLE 1 Rules for driving behavior characterization If accel If brake Ifgap-time pedal rate pedal rate Then driver is STD is STD is index is LowLow Low Less Aggressive High Low Low Cautious Low High Low AggressiveLow Low High Aggressive Low High High Aggressive High High High LessAggressive High Low High Cautious High High Low Less Aggressive

FIGS. 16A and 16B show the driver index computed from two runs ofvehicle testing data: one for aggressive driving with a driver indexgreater than 0.8 and the other for cautious driving with a driver indexless than 0.2.

VII. Supervised Driving Style Characterization

The car-following task requires the driver to maintain with the leadingvehicle one of the following (i) a zero speed difference; (ii) aconstant relative distance; and (iii) a constant relative gap-timedefined by the division of the relative distance by the relativevelocity.

A human driver may be modeled as a PD feedback controller. The closedloop system during a car following maneuver may be expressed as({umlaut over (x)} _(l) −{umlaut over (x)} _(f) −{umlaut over (x)}_(g))=−c _(v)({dot over (x)} _(l) −{dot over (x)} _(f) −{dot over (x)}_(g))−c _(s)(x _(l) −x _(f) −x _(g))  (28)where x_(l) and x_(f) are the leading and the following vehicle traveldistance and x _(g) is the gap offset reference. Due to theimplementation of radar used in vehicles equipped with adaptive cruisecontrol function, the relative distance and velocity are measured anddefined asΔs=−x _(l) −x _(f) ,Δv={dot over (x)} _(l) −{dot over (x)} _(f)  (29)A vehicle equipped with stability controls has a longitudinalaccelerometer with output α_(x), which measures {umlaut over (x)}_(f).(28) can be further expressed asα_(x) =c _(v)(Δv−{dot over (x)} )+c _(s)(Δs−x _(g))+({umlaut over (x)}_(l) −{umlaut over (x)} _(g))  (30)The unknown parameters c_(v) and c_(s) in (30) can be used tocharacterize a driver's control structure during a car following. Usingthe low-pass filtered Δs and Δv to replace the gap offset reference x_(g) and its derivative {dot over (x)} _(g), and considering the timedelays, we have the following equations

$\begin{matrix}{\alpha_{x_{k + i}} = {{{c_{v}\lbrack {{\Delta\; s_{k}} - {\mu_{k}( {\Delta\; s} )}} \rbrack} + {c_{s}\lbrack {{\Delta\; v_{k}} - {\mu_{k}( {\Delta\; v} )}} \rbrack} + {w\begin{bmatrix}{\mu_{k}( {\Delta\; s} )} \\{\mu_{k}( {\Delta\; v} )}\end{bmatrix}}} = {{( {1 - \alpha} )\begin{bmatrix}{\mu_{k - 1}( {\Delta\; s} )} \\{\mu_{k - 1}( {\Delta\; v} )}\end{bmatrix}} + {\alpha\begin{bmatrix}{\Delta\; s_{k}} \\{\Delta\; v_{k}}\end{bmatrix}}}}} & (31)\end{matrix}$where subscript i in α_(x) _(k+i) reflects the time delay between thedriver's braking/throttling actuation and the measured relative distanceand velocity, and acceleration, α is a low-pass filter coefficientsimilar to the one used in (22), and w is a high frequency uncertainsignal that may be treated as white noise. Using a conditional leastsquare identification algorithm, c_(v) and c_(s) can be identified inreal-time from (31). The response time t_(p) and the damping ratio ζ ofthe driver-in-the-loop system can be related to c_(v) and c_(s) ast _(p)=2πc _(s)/√{square root over (4c _(s) −c _(c) ²,)}ζ=c_(v)/2√{square root over (c _(s))}  (32)which can be used to deduce the driver's driving behavior: (i) for anormal driver, it is desired that the transient response of thedriver-in-the-loop system be fast (sufficiently small t_(p), e.g., lessthan 0.5 s) and damped (sufficiently large ζ); (ii) for an aged ordriver with physical limitation, t_(p) may be large; (iii) for anaggressive driver, ζ is likely to show a small value, such as one lessthan 0.5, and the system response is likely to exhibit excessiveovershoot; (iv) for a cautious driver, ζ is likely to show a reasonablylarge value, such as one greater than 0.7.

A least-square parameter identification was implemented for calculatingc_(v) and c_(s). Two runs of vehicle testing were conducted. In the1^(st) run, the driver in the following car tried to use aggressivethrottling and braking to achieve a constant relative gap-time betweenhis vehicle and a leading vehicle, which led to larger range errorΔs_(k)−μ_(k)(Δs). See, FIG. 17. The identified c_(v) is around 0.2 andidentified c, around 0.05. See, FIG. 18. The damping ratio thus computedfrom (32) showed a value less than 0.5, which is an indication of alight damping driver-in-the-loop system, hence corresponding toaggressive driving behavior.

In the 2nd run, the driver was using cautious throttle and brakeapplication to achieve car following, the relative range errorΔs_(k)−μ_(k)(Δs) in FIG. 19 had less magnitude in comparison with theone shown in FIG. 17. The identified c_(v) and c_(s) are depicted inFIG. 20. The damping ratio showed a value greater than 0.8 except duringthe first 150 seconds. See, FIG. 20. This is an indication of a heavydamping driver-in-the-loop system, hence corresponding to cautiousdriving behavior.

VIII. Applications

The handling limit and/or driving style characterization, with propermonitoring and re-enforcement, may be used for driver advising,coaching, monitoring and safety enforcement. Another possibleapplication relates to the opportunity for vehicle personalizationthrough tuning of control parameters to fit the specific driver's style.For example, the ESC or brake control system can exploit such a driverstyle characterization to adapt the actuation threshold to fit thepersonal driving behavior. As an example, an expert driver might needless frequent ESC activations compared to a less experienced driver infacing the same driving conditions. (There may, however, be a minimumrequirement for adjusting the thresholds such that a mistake by anexpert driver can still be assisted by the ESC function).

As another example, steering sensitivity (the degree of vehicle steeringresponse to a given steering input) and accelerator pedal sensitivity(the degree of vehicle acceleration response to a given acceleratorpedal input) may be tuned based on the driver characterization. Thesteering wheel and/or accelerator pedal may be made more sensitive ifthe driver is characterized as an expert (providing greater vehicleresponsiveness). The steering wheel and/or accelerator pedal may be madeless sensitive if the driver is characterized as cautious (potentiallyleading to improved fuel economy). Other applications are also possible.

FIG. 21 is a block diagram of an embodiment of an advisory system 30 fora vehicle 32. The advisory system 30 may include a plurality of vehiclestate sensors 34 (e.g., yaw rate sensor, steering angle sensor, lateralacceleration sensor, longitudinal acceleration sensor, wheel speedsensor, brake pressure sensor, etc.), one or more controllers 36configured to perform, for example, electronic stability control,anti-lock braking and traction control as well as the handling limit anddriver characterization described above, an audio and/or visualindicator system 38 (e.g., a display panel, speaker system, LED array,USB port, etc.), and a vehicle input and/or control system 40 (e.g.,accelerator pedal, powertrain controller, steering wheel, etc.)

The vehicle state sensors 34 may detect the various parameters describedabove, such as vehicle speed, wheel slip, etc., that characterize themotion of the vehicle 32 (e.g., current handling condition and limithandling condition) as well as the driver inputs described above (e.g.,accelerator and brake pedal position, etc.) The one or more controllers36 may use this information as inputs to the handling limit and/ordriver characterization algorithms described above. Based on the outputof these algorithms, the one or more controllers 36, as described above,may (i) activate the audio and/or visual indicator system 38 to, forexample, alert or coach the driver or (ii) modify aspects of the vehicleinput and/or control system 40 to customize the vehicle's response tothe type of driver.

As an example, based on information gathered by the sensors 34, the oneor more controllers 36, executing the algorithms described above, maydetermine that a driver of the vehicle 32 is driving recklessly. Becausethe driver has been classified as reckless, the one or more controllers36 may, for example, begin to issue driving instructions via the audioand/or visual indicator system 38 to encourage the driver to changetheir behavior. The one or more controllers 36 may also/instead activatehaptic elements of the vehicle input and/or control system 40, such as ahaptic accelerator pedal, to alert the driver of their recklessbehavior.

A memory accessible by the one or more controllers 36 may include adatabase of instructions (audio and/or visual) that are mapped withcertain predefined rules. An example rule may be that if a driver isreckless and the rate of steering wheel angle change falls within somedefined range for a specified period of time (that is, the drivercontinues to rapidly rotate the steering wheel clockwise andcounterclockwise), then instruct the driver to reduce their steeringinputs.

FIG. 22 is a block diagram of another embodiment of an advisory system130 for a vehicle 132, where like numerals have descriptions similar tothose of FIG. 21. In this embodiment, however, the vehicle 132 includesa token recognition system 142 configured in a known fashion torecognize a token 144. As an example, the token 144 may be a keyincluding an identification chip identifying a particular driver orclass of drivers (e.g., teen drivers). In this example, the tokenrecognition system 142 may include a chip reader arranged in a knownfashion within an ignition system of the vehicle 132 to read theidentification chip and communicate this information to the one or morecontrollers 136. As another example, the token 144 may be a key-fob orplastic card having an RFID chip embedded therein. In this example, thetoken recognition system 142 may include an RFID chip reader arranged ina known fashion within the vehicle 132 to detect and read the RFID chipand communicate this information to the one or more controllers 136. Asyet another example, the token 144 may be a cell phone. In this example,the token recognition system 142 may include known modules (such asFord's SYNC technology) configured to recognize the cell phone andcommunicate this information to the one or more controllers 136. Otherarrangements and scenarios are also possible.

A variety of functions may be implemented based on driver identificationand the handling limit and/or driving style characterization describedabove. As an example, if the token recognition system 142 suppliesinformation to the one or more controllers 136 identifying the driver asa teen driver, the one or more controllers 136 may issue instructions tothe driver via the audio and/or visual indicator system 138 to dropthrottle or to brake as the handling limit is being approached. The oneor more controllers 136 may implement rule based commands similar tothose described above to effectuate such driving instruction. (Anexample rule may be that if the driver is a teen driver and if thehandling limit margin is less than 15%, then instruct the driver to slowdown.)

As another example, if the token recognition system 142 suppliesinformation to the one or more controllers 136 identifying the driver asa teen driver, the one or more controllers 136 may record a history ofthe calculations of handling limit margin and/or driver stylecharacterization in order to generate reports describing drivingbehavior. These reports, for example, may detail, for a given trip, thenumber of times the teen driver exceeded certain levels of the handlinglimit margin. These reports, for example, may also describe the teendriver as cautious, aggressive, reckless, etc. during any given trip.Such reports may be accessed, reported or displayed in anysuitable/known fashion, such as through the audio and/or visualindicator system 138.

As yet another example, driver coaching and/or instruction may beimplemented based on the driver identification described above. The oneor more controllers 136 may, for example, issue instructions toencourage a driver to provide driving inputs to the vehicle 132 thatwill result in the driver being classified as cautious. If, for example,a driver begins to frequently accelerate and decelerate, the one or morecontrollers 136 may instruct the driver via the audio and/or visualindicator system 138 to increase the distance between the vehicle 132and vehicle in front of them so as to reduce the frequency ofacceleration and braking. Rules similar to those described above, or anyother suitable intelligence technology such as neural networks, etc. maybe used to facilitate the instructions. In embodiments where drivingbehavior is recorded for later reporting, whether the driver heeds orignores the instructions may also be recorded as an indication of thedriver's behavior.

FIG. 23 is a block diagram of yet another embodiment of an advisorysystem 230 for a vehicle 232, where like numerals have descriptionssimilar to those of FIG. 21. This embodiment includes a radar and/orcamera system 246 (any suitable forward sensing system, however, may beused) that may periodically/continuously detect, in a known fashion, thedistance between the vehicle 232 and another vehicle in front of thevehicle 232. (Although not shown, the advisory system 230 may alsoinclude a token recognition system and associated capabilities similarto that discussed with reference to FIG. 22. Other configurations arealso possible.)

In certain circumstances, the distance information collected by thesystem 246 may be monitored by the one or more controllers 236. If thedistance is less than some predefined threshold (e.g., 20 feet), the oneor more controllers 236 may warn the driver via the audio and/or videoindicator system 238 and/or activate elements of the vehicle inputand/or control systems 240 if they are of the haptic type (e.g., hapticaccelerator pedal, haptic steering wheel, haptic seat, etc.)

In other circumstances, the distance information, X, collected by thesystem 246, together with the change in distance over time, V_(x), andlongitudinal acceleration of the vehicle 232, A_(x), may used by the oneor more controllers 236 to determine a time to collision, t_(c), withthe vehicle in front of the vehicle 232 by the following equation

$t_{c} = \frac{{- V_{x}} \pm \sqrt{( V_{x} )^{2} + {2( A_{x} )(X)}}}{( A_{x} )}$${{or}\mspace{14mu} t_{c}} = \frac{X}{V_{x}}$If the time to collision is less than a predefined threshold, the one ormore controllers 236 may warn the driver as described above.

In still other circumstances, the one or more controllers 236 may basethe warning threshold on the gap time (discussed above) between thevehicle 232 and the vehicle in front of it. If the gap time is less thana threshold value, the one or more controllers 236 may, for example,activate (vibrate) haptic elements of the vehicle input and/or controlsystems 240, etc.

Alternatively, the one or more controllers 236 may access tables ofdistance and speed/relative speed to determine when to warn the driver.As an example, if the distance falls within a certain range and thespeed falls within a certain range, the one or more controllers 236 mayactivate a haptic accelerator pedal. Other scenarios are also possible.

The intensity (frequency and/or amplitude) with which haptic elements ofthe vehicle input and/or control systems 240 are activated may depend onthe distance, time to collision, gap time, etc. between the vehicle 232and the vehicle in front of it. As an example, the intensity mayincrease as these parameters decrease. This increasing intensity maysignal increasing urgency.

The predefined thresholds may depend on the type of driver. That is, theone or more controllers 236 may implement the driver characterizationalgorithms discussed above and, based on the characterization, increaseor decrease the warning threshold. A threshold may be decreased, forexample, for an expert driver because they may be less likely toexperience an accident as a result of tailgating. A threshold may beincreased for a reckless or aggressive driver because they may be morelikely to experience an accident as a result of tailgating, etc.

The predefined thresholds may be altered based on whether the driverheeds the warning they indicate. As an example, if a driver does notincrease their following distance to the vehicle in front of them afteractivation of a haptic accelerator pedal, the predefined followingdistance that is used to trigger the activation of the haptic pedal maybe decreased so as to avoid becoming a nuisance to the driver. Thepredefined thresholds associated with time to collision and gap time maybe similarly decreased. A minimum, however, may be established beyondwhich the threshold will not be decreased.

As apparent to those of ordinary skill, the algorithms disclosed hereinmay be deliverable to a processing device, which may include anyexisting electronic control unit or dedicated electronic control unit,in many forms including, but not limited to, information permanentlystored on non-writable storage media such as ROM devices and informationalterably stored on writeable storage media such as floppy disks,magnetic tapes, CDs, RAM devices, and other magnetic and optical media.The algorithms may also be implemented in a software executable object.Alternatively, the algorithms may be embodied in whole or in part usingsuitable hardware components, such as Application Specific IntegratedCircuits (ASICs), state machines, controllers or other hardwarecomponents or devices, or a combination of hardware, software andfirmware components.

While embodiments of the invention have been illustrated and described,it is not intended that these embodiments illustrate and describe allpossible forms of the invention. The words used in the specification arewords of description rather than limitation, and it is understood thatvarious changes may be made without departing from the spirit and scopeof the invention.

What is claimed:
 1. A vehicle comprising: at least one of an electronicstability control system, anti-lock braking system and traction controlsystem; a plurality of sensors configured to measure parametersrepresenting the vehicle's current handling condition and the vehicle'slimit handling condition, wherein each of the parameters has upper andlower deadband thresholds defining a deadband interval within which themeasured parameter may fall without the at least one electronicstability control system, anti-lock braking system and traction controlsystem being activated; and at least one computing device operativelyarranged with the sensors and configured (i) to determine, for each ofthe parameters, a normalized difference between the parameter and atleast one of the parameter's upper and lower deadband thresholds, (ii)to identify a minimum of the normalized differences, and (iii) toinitiate an alert for a driver of the vehicle if the minimum of thenormalized differences exceeds a predetermined threshold within thedeadband interval.
 2. The vehicle of claim 1 further comprising a hapticpedal, wherein initiating an alert for a driver of the vehicle includesactivating the haptic pedal.
 3. The vehicle of claim 1 furthercomprising at least one of an audio, visual and haptic driver interfaceoperatively arranged with the at least one computing device, whereininitiating an alert for the driver includes activating the at least oneof audio, visual and haptic driver interface.
 4. The vehicle of claim 1wherein the sensors include at least one of a yaw rate sensor, steeringangle sensor, lateral acceleration sensor, longitudinal accelerationsensor, wheel speed sensor, and brake pressure sensor.
 5. The vehicle ofclaim 1 wherein the computing device is further configured to low-passfilter the minimum of the normalized differences.